Interactive leafmap

In [1]:

import ecospat.mapping as ecospat_ipyleaflet
import leafmap

import ecospat.mapping as ecospat_ipyleaflet import leafmap

Creating an interactive map with ecospat package¶

In [2]:

interactive_map = ecospat_ipyleaflet.Map(center=[40, -100], zoom=4, height="300px")

url = "https://nominatim.openstreetmap.org/search?format=json&q={s}"
interactive_map.add_search_control(url, zoom=10, position="topleft")
interactive_map

interactive_map = ecospat_ipyleaflet.Map(center=[40, -100], zoom=4, height="300px") url = "https://nominatim.openstreetmap.org/search?format=json&q={s}" interactive_map.add_search_control(url, zoom=10, position="topleft") interactive_map

Out[2]:

Adding a WMS layer with a legend¶

In [3]:

legend_map = leafmap.Map(center=[40, -100], zoom=4, height="500px")
wms_url = "https://services.terrascope.be/wms/v2?"
wms_layer = "WORLDCOVER_2021_MAP"

# Add the ESA WorldCover layer
legend_map.add_wms_layer(
    url=wms_url,
    layers=wms_layer,
    name="ESA WorldCover 2021",
    attribution="ESA/Terrascope",
    format="image/png",
    transparent=True,
    shown=True,
)

legend_map.add_legend(
    title="ESA WorldCover", legend_dict=leafmap.builtin_legends["ESA_WorldCover"]
)

legend_map

legend_map = leafmap.Map(center=[40, -100], zoom=4, height="500px") wms_url = "https://services.terrascope.be/wms/v2?" wms_layer = "WORLDCOVER_2021_MAP" # Add the ESA WorldCover layer legend_map.add_wms_layer( url=wms_url, layers=wms_layer, name="ESA WorldCover 2021", attribution="ESA/Terrascope", format="image/png", transparent=True, shown=True, ) legend_map.add_legend( title="ESA WorldCover", legend_dict=leafmap.builtin_legends["ESA_WorldCover"] ) legend_map

Out[3]:

Creating cluster markers¶

In [4]:

import geopandas as gpd

url = "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson"

# Read the GeoJSON file
gdf = gpd.read_file(url)

# Add latitude and longitude columns from the geometry
gdf["longitude"] = gdf.geometry.x
gdf["latitude"] = gdf.geometry.y

m = leafmap.Map(center=[47.654, -117.60], zoom=16)
m.add_basemap("Google Satellite")
m.add_marker_cluster(gdf, x="longitude", y="latitude", layer_name="Buildings")
m

import geopandas as gpd url = "https://github.com/opengeos/datasets/releases/download/places/wa_building_centroids.geojson" # Read the GeoJSON file gdf = gpd.read_file(url) # Add latitude and longitude columns from the geometry gdf["longitude"] = gdf.geometry.x gdf["latitude"] = gdf.geometry.y m = leafmap.Map(center=[47.654, -117.60], zoom=16) m.add_basemap("Google Satellite") m.add_marker_cluster(gdf, x="longitude", y="latitude", layer_name="Buildings") m

Out[4]:

Creating circle markers¶

In [5]:

m2 = leafmap.Map(center=[47.654, -117.60], zoom=16)
m2.add_basemap("Google Satellite")
m2.add_circle_markers_from_xy(
    gdf,
    x="longitude",
    y="latitude",
    layer_name="Buildings",
    radius=5,
    fill_color="yellow",
    fill_opacity=0.8,
    color="red",
)
m2

m2 = leafmap.Map(center=[47.654, -117.60], zoom=16) m2.add_basemap("Google Satellite") m2.add_circle_markers_from_xy( gdf, x="longitude", y="latitude", layer_name="Buildings", radius=5, fill_color="yellow", fill_opacity=0.8, color="red", ) m2

Out[5]:

Visualizing vector data - polygons¶

In [6]:

m3 = leafmap.Map(center=[47.654, -117.60], zoom=16)


style = {"color": "red"}

m3.add_basemap("Google Satellite")
m3.add_vector(
    "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson",
    style=style,
    layer_name="Building Outlines",
)
m3

m3 = leafmap.Map(center=[47.654, -117.60], zoom=16) style = {"color": "red"} m3.add_basemap("Google Satellite") m3.add_vector( "https://github.com/opengeos/datasets/releases/download/places/wa_overture_buildings.geojson", style=style, layer_name="Building Outlines", ) m3

Out[6]:

Visualizing vector data - lines¶

In [7]:

m4 = leafmap.Map(center=[36.121, -115.205], zoom=17)

style = {
    "color": "red",  # outline color
    "weight": 2,  # outline thickness    # fully transparent
}

m4.add_basemap("Google Satellite")
m4.add_vector(
    "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson",
    style=style,
    layer_name="Las Vegas Roads",
)
m4

m4 = leafmap.Map(center=[36.121, -115.205], zoom=17) style = { "color": "red", # outline color "weight": 2, # outline thickness # fully transparent } m4.add_basemap("Google Satellite") m4.add_vector( "https://github.com/opengeos/datasets/releases/download/places/las_vegas_roads.geojson", style=style, layer_name="Las Vegas Roads", ) m4

Out[7]:

Visualizing vector data - data¶

In [8]:

m5 = leafmap.Map(center=[40, -100], zoom=4)


url = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson"
gdf = gpd.read_file(url)

# Create the choropleth map based on the CENSUSAREA column
m5.add_data(
    gdf,
    column="CENSUSAREA",
    cmap="Blues",
    layer_name="Census Area",
    legend_title="Census Area",
    legend=True,
)

# Display the map
m5

m5 = leafmap.Map(center=[40, -100], zoom=4) url = "https://github.com/opengeos/datasets/releases/download/us/us_counties.geojson" gdf = gpd.read_file(url) # Create the choropleth map based on the CENSUSAREA column m5.add_data( gdf, column="CENSUSAREA", cmap="Blues", layer_name="Census Area", legend_title="Census Area", legend=True, ) # Display the map m5

Out[8]:

Creating a split map¶

In [9]:

m6 = leafmap.Map()
m6.add_basemap("Satellite")
image1 = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif"
)
image2 = (
    "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif"
)
m6.split_map(
    image1,
    image2,
    left_label="Pre-event",
    right_label="Post-event",
)
m6

m6 = leafmap.Map() m6.add_basemap("Satellite") image1 = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-07-01.tif" ) image2 = ( "https://github.com/opengeos/datasets/releases/download/raster/Libya-2023-09-13.tif" ) m6.split_map( image1, image2, left_label="Pre-event", right_label="Post-event", ) m6

Out[9]: